How can I debug Django database queries and identify performance bottlenecks?
I'm working on a Django project and encountering an issue with Django views. Here's my current implementation:
# models.py
# views.py
from django.shortcuts import render
from .models import Article
def article_list(request):
    articles = Article.objects.all()
    for article in articles:
        print(article.author.username)  # N+1 problem here
    return render(request, 'articles.html', {'articles': articles})
The specific error I'm getting is: "django.urls.exceptions.NoReverseMatch: Reverse for 'article_detail' not found"
I've already tried the following approaches:
- Checked Django documentation and Stack Overflow
- Verified my database schema and migrations
- Added debugging prints to trace the issue
- Tested with different data inputs
Environment details:
- Django version: 5.0.1
- Python version: 3.11.0
- Database: PostgreSQL 15
- Operating system: Ubuntu 22.04
Has anyone encountered this before? Any guidance would be greatly appreciated!
Comments
admin: Could you elaborate on the select_related vs prefetch_related usage? When should I use each? 2 months ago
5 Answers
The difference between threading and multiprocessing in Python is crucial for performance:
Threading (shared memory, GIL limitation):
import threading
import time
def io_bound_task(name):
    print(f'Starting {name}')
    time.sleep(2)  # Simulates I/O operation
    print(f'Finished {name}')
# Good for I/O-bound tasks
threads = []
for i in range(3):
    t = threading.Thread(target=io_bound_task, args=(f'Task-{i}',))
    threads.append(t)
    t.start()
for t in threads:
    t.join()Multiprocessing (separate memory, no GIL):
import multiprocessing
import time
def cpu_bound_task(name):
    # CPU-intensive calculation
    result = sum(i * i for i in range(1000000))
    return f'{name}: {result}'
# Good for CPU-bound tasks
if __name__ == '__main__':
    with multiprocessing.Pool(processes=4) as pool:
        tasks = [f'Process-{i}' for i in range(4)]
        results = pool.map(cpu_bound_task, tasks)
        print(results)Concurrent.futures (unified interface):
from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
# For I/O-bound tasks
with ThreadPoolExecutor(max_workers=4) as executor:
    futures = [executor.submit(io_bound_task, f'Task-{i}') for i in range(4)]
    results = [future.result() for future in futures]
# For CPU-bound tasks
with ProcessPoolExecutor(max_workers=4) as executor:
    futures = [executor.submit(cpu_bound_task, f'Process-{i}') for i in range(4)]
    results = [future.result() for future in futures]Comments
abadi: Could you provide the requirements.txt for the packages used in this solution? 2 months ago
Python decorators with arguments require a three-level nested function. Here's the proper implementation:
import functools
# Decorator with arguments
def retry(max_attempts=3, delay=1):
    def decorator(func):
        @functools.wraps(func)  # Preserves function metadata
        def wrapper(*args, **kwargs):
            for attempt in range(max_attempts):
                try:
                    return func(*args, **kwargs)
                except Exception as e:
                    if attempt == max_attempts - 1:
                        raise e
                    time.sleep(delay)
        return wrapper
    return decorator
# Usage
@retry(max_attempts=5, delay=2)
def unreliable_function():
    # Function that might fail
    passClass-based decorator (alternative approach):
class Retry:
    def __init__(self, max_attempts=3, delay=1):
        self.max_attempts = max_attempts
        self.delay = delay
    
    def __call__(self, func):
        @functools.wraps(func)
        def wrapper(*args, **kwargs):
            for attempt in range(self.max_attempts):
                try:
                    return func(*args, **kwargs)
                except Exception as e:
                    if attempt == self.max_attempts - 1:
                        raise e
                    time.sleep(self.delay)
        return wrapper
# Usage
@Retry(max_attempts=5, delay=2)
def another_function():
    passThe RecursionError occurs when Python's recursion limit is exceeded. Here are several solutions:
1. Increase recursion limit (temporary fix):
import sys
sys.setrecursionlimit(10000)  # Default is usually 10002. Convert to iterative approach (recommended):
# Recursive (problematic for large inputs)
def factorial_recursive(n):
    if n <= 1:
        return 1
    return n * factorial_recursive(n - 1)
# Iterative (better)
def factorial_iterative(n):
    result = 1
    for i in range(2, n + 1):
        result *= i
    return result3. Use memoization for recursive algorithms:
from functools import lru_cache
@lru_cache(maxsize=None)
def fibonacci(n):
    if n < 2:
        return n
    return fibonacci(n-1) + fibonacci(n-2)4. Tail recursion optimization (manual):
def factorial_tail_recursive(n, accumulator=1):
    if n <= 1:
        return accumulator
    return factorial_tail_recursive(n - 1, n * accumulator)To handle Django database transactions properly and avoid data inconsistency, use Django's transaction management:
from django.db import transaction
# Method 1: Decorator
@transaction.atomic
def transfer_money(from_account, to_account, amount):
    from_account.balance -= amount
    from_account.save()
    
    to_account.balance += amount
    to_account.save()
# Method 2: Context manager
def complex_operation():
    with transaction.atomic():
        # All operations in this block are atomic
        user = User.objects.create(username='test')
        profile = UserProfile.objects.create(user=user)
        # If any operation fails, all are rolled backFor more complex scenarios with savepoints:
def nested_transactions():
    with transaction.atomic():
        # Outer transaction
        user = User.objects.create(username='test')
        
        try:
            with transaction.atomic():
                # Inner transaction (savepoint)
                risky_operation()
        except Exception:
            # Inner transaction rolled back, outer continues
            handle_error()The RecursionError occurs when Python's recursion limit is exceeded. Here are several solutions:
1. Increase recursion limit (temporary fix):
import sys
sys.setrecursionlimit(10000)  # Default is usually 10002. Convert to iterative approach (recommended):
# Recursive (problematic for large inputs)
def factorial_recursive(n):
    if n <= 1:
        return 1
    return n * factorial_recursive(n - 1)
# Iterative (better)
def factorial_iterative(n):
    result = 1
    for i in range(2, n + 1):
        result *= i
    return result3. Use memoization for recursive algorithms:
from functools import lru_cache
@lru_cache(maxsize=None)
def fibonacci(n):
    if n < 2:
        return n
    return fibonacci(n-1) + fibonacci(n-2)4. Tail recursion optimization (manual):
def factorial_tail_recursive(n, accumulator=1):
    if n <= 1:
        return accumulator
    return factorial_tail_recursive(n - 1, n * accumulator)Your Answer
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